{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.比特币价格预测及绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>btc_market_price</th>\n",
       "      <th>btc_total_bitcoins</th>\n",
       "      <th>btc_market_cap</th>\n",
       "      <th>btc_trade_volume</th>\n",
       "      <th>btc_blocks_size</th>\n",
       "      <th>btc_avg_block_size</th>\n",
       "      <th>btc_n_orphaned_blocks</th>\n",
       "      <th>btc_n_transactions_per_block</th>\n",
       "      <th>btc_median_confirmation_time</th>\n",
       "      <th>...</th>\n",
       "      <th>btc_cost_per_transaction_percent</th>\n",
       "      <th>btc_cost_per_transaction</th>\n",
       "      <th>btc_n_unique_addresses</th>\n",
       "      <th>btc_n_transactions</th>\n",
       "      <th>btc_n_transactions_total</th>\n",
       "      <th>btc_n_transactions_excluding_popular</th>\n",
       "      <th>btc_n_transactions_excluding_chains_longer_than_100</th>\n",
       "      <th>btc_output_volume</th>\n",
       "      <th>btc_estimated_transaction_volume</th>\n",
       "      <th>btc_estimated_transaction_volume_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-23 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2110700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25100.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>42613.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-24 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2120200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>179.245283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>42809.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>5300.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-02-25 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2127600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1057.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>42959.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-02-26 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2136100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64.582059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>43135.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>29349.0</td>\n",
       "      <td>13162.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-27 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2144750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1922.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>43311.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>9101.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  btc_market_price  btc_total_bitcoins  btc_market_cap  \\\n",
       "0  2010-02-23 00:00:00               0.0           2110700.0             0.0   \n",
       "1  2010-02-24 00:00:00               0.0           2120200.0             0.0   \n",
       "2  2010-02-25 00:00:00               0.0           2127600.0             0.0   \n",
       "3  2010-02-26 00:00:00               0.0           2136100.0             0.0   \n",
       "4  2010-02-27 00:00:00               0.0           2144750.0             0.0   \n",
       "\n",
       "   btc_trade_volume  btc_blocks_size  btc_avg_block_size  \\\n",
       "0               0.0              0.0            0.000216   \n",
       "1               0.0              0.0            0.000282   \n",
       "2               0.0              0.0            0.000227   \n",
       "3               0.0              0.0            0.000319   \n",
       "4               0.0              0.0            0.000223   \n",
       "\n",
       "   btc_n_orphaned_blocks  btc_n_transactions_per_block  \\\n",
       "0                    0.0                           1.0   \n",
       "1                    0.0                           1.0   \n",
       "2                    0.0                           1.0   \n",
       "3                    0.0                           1.0   \n",
       "4                    0.0                           1.0   \n",
       "\n",
       "   btc_median_confirmation_time  ...  btc_cost_per_transaction_percent  \\\n",
       "0                           0.0  ...                      25100.000000   \n",
       "1                           0.0  ...                        179.245283   \n",
       "2                           0.0  ...                       1057.142857   \n",
       "3                           0.0  ...                         64.582059   \n",
       "4                           0.0  ...                       1922.222222   \n",
       "\n",
       "   btc_cost_per_transaction  btc_n_unique_addresses  btc_n_transactions  \\\n",
       "0                       0.0                   252.0               252.0   \n",
       "1                       0.0                   195.0               196.0   \n",
       "2                       0.0                   150.0               150.0   \n",
       "3                       0.0                   176.0               176.0   \n",
       "4                       0.0                   176.0               176.0   \n",
       "\n",
       "   btc_n_transactions_total  btc_n_transactions_excluding_popular  \\\n",
       "0                   42613.0                                 252.0   \n",
       "1                   42809.0                                 196.0   \n",
       "2                   42959.0                                 150.0   \n",
       "3                   43135.0                                 176.0   \n",
       "4                   43311.0                                 176.0   \n",
       "\n",
       "   btc_n_transactions_excluding_chains_longer_than_100  btc_output_volume  \\\n",
       "0                                              252.0              12600.0   \n",
       "1                                              196.0              14800.0   \n",
       "2                                              150.0               8100.0   \n",
       "3                                              176.0              29349.0   \n",
       "4                                              176.0               9101.0   \n",
       "\n",
       "   btc_estimated_transaction_volume  btc_estimated_transaction_volume_usd  \n",
       "0                              50.0                                   0.0  \n",
       "1                            5300.0                                   0.0  \n",
       "2                             700.0                                   0.0  \n",
       "3                           13162.0                                   0.0  \n",
       "4                             450.0                                   0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pandata = pd.read_csv('../data/challenge-2-bitcoin.csv', header=0)\n",
    "\n",
    "pandata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>btc_market_price</th>\n",
       "      <th>btc_total_bitcoins</th>\n",
       "      <th>btc_transaction_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2110700.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2120200.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2127600.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2136100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2144750.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   btc_market_price  btc_total_bitcoins  btc_transaction_fees\n",
       "0               0.0           2110700.0                   0.0\n",
       "1               0.0           2120200.0                   0.0\n",
       "2               0.0           2127600.0                   0.0\n",
       "3               0.0           2136100.0                   0.0\n",
       "4               0.0           2144750.0                   0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 挑战：分离出仅包含 btc_market_price，btc_total_bitcoins，btc_transaction_fees 列的 DataFrame，并定义为变量 data。\n",
    "\n",
    "data = pandata[['btc_market_price', 'btc_total_bitcoins', 'btc_transaction_fees']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=2920, step=1) 0\n",
      "RangeIndex(start=0, stop=2920, step=1) 1\n",
      "RangeIndex(start=0, stop=2920, step=1) 2\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 练习5.3\n",
    "# 挑战：分别绘制 data 数据集 3 列数据的线形图，并以横向子图排列。\n",
    "# 规定：需设置各图横纵轴名称，横轴统一为 time，纵轴为各自列名称。\n",
    "# 提示：使用 set_xlabel() 设置横轴名称。\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharex=True)\n",
    "\n",
    "columns = data.columns\n",
    "for i, col in enumerate(columns):\n",
    "    axes[i].plot(data.index, data[col])\n",
    "    axes[i].set_title(col)\n",
    "    axes[i].set_xlabel('time')\n",
    "    axes[i].set_ylabel(col)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下是代码逐行的详细解释：\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 导入 `matplotlib.pyplot` 模块，用于创建各种类型的图表。\n",
    "- `plt` 是 `matplotlib.pyplot` 的常用别名。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5), sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用 `plt.subplots()` 创建一个包含多个子图的图表。\n",
    "  - `1, 3`：表示创建 1 行 3 列的子图布局。\n",
    "  - `figsize=(18, 5)`：设置整个图表的宽度为 18 英寸，高度为 5 英寸。\n",
    "  - `sharex=True`：表示所有子图共享相同的 x 轴（横轴）。\n",
    "- 返回值：\n",
    "  - `fig`：表示整个图表对象。\n",
    "  - `axes`：一个包含 3 个子图（`Axes` 对象）的数组。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "columns = data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 获取 `data` 数据集的列名，并存储在变量 `columns` 中。\n",
    "- `data.columns` 是一个包含所有列名的索引对象（通常是字符串列表）。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for i, col in enumerate(columns):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 使用 `enumerate()` 遍历 `columns`，同时获取每个列名 `col` 和对应的索引 `i`。\n",
    "- `i` 表示当前列的索引（从 0 开始）。\n",
    "- `col` 表示当前列的名称。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    axes[i].plot(data.index, data[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 在第 `i` 个子图上绘制当前列的数据。\n",
    "  - `data.index`：表示 x 轴的值（通常是时间或索引）。\n",
    "  - `data[col]`：表示 y 轴的值（当前列的数据）。\n",
    "- `axes[i]` 表示第 `i` 个子图。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    axes[i].set_title(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 设置第 `i` 个子图的标题为当前列的名称 `col`。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    axes[i].set_xlabel('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 设置第 `i` 个子图的 x 轴标签为 `'time'`。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    axes[i].set_ylabel(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 设置第 `i` 个子图的 y 轴标签为当前列的名称 `col`。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 调整子图之间的间距，避免标题、轴标签等内容重叠。\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 显示图表。\n",
    "\n",
    "---\n",
    "\n",
    "### 总结\n",
    "这段代码的功能是：\n",
    "1. 创建一个包含 1 行 3 列子图的图表。\n",
    "2. 遍历 `data` 数据集的每一列，在每个子图中绘制对应列的线形图。\n",
    "3. 为每个子图设置标题、x 轴标签（统一为 `'time'`）和 y 轴标签（为列名）。\n",
    "4. 调整布局并显示图表。\n",
    "\n",
    "最终输出是一个横向排列的 3 个子图，每个子图展示 `data` 数据集中一列数据随时间变化的线形图。"
   ]
  }
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